Quantile Regression with a New Exponentiated Odd Log-Logistic Weibull Distribution
We define a new quantile regression model based on a reparameterized exponentiated odd log-logistic Weibull distribution, and obtain some of its structural properties. It includes as sub-models some known regression models that can be utilized in many areas. The maximum likelihood method is adopted...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-03-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/11/6/1518 |
| _version_ | 1797610289934368768 |
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| author | Gabriela M. Rodrigues Edwin M. M. Ortega Gauss M. Cordeiro Roberto Vila |
| author_facet | Gabriela M. Rodrigues Edwin M. M. Ortega Gauss M. Cordeiro Roberto Vila |
| author_sort | Gabriela M. Rodrigues |
| collection | DOAJ |
| description | We define a new quantile regression model based on a reparameterized exponentiated odd log-logistic Weibull distribution, and obtain some of its structural properties. It includes as sub-models some known regression models that can be utilized in many areas. The maximum likelihood method is adopted to estimate the parameters, and several simulations are performed to study the finite sample properties of the maximum likelihood estimators. The applicability of the proposed regression model is well justified by means of a gastric carcinoma dataset. |
| first_indexed | 2024-03-11T06:13:22Z |
| format | Article |
| id | doaj.art-6bbed3537f78441a94913704f520bfc6 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-11T06:13:22Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-6bbed3537f78441a94913704f520bfc62023-11-17T12:29:45ZengMDPI AGMathematics2227-73902023-03-01116151810.3390/math11061518Quantile Regression with a New Exponentiated Odd Log-Logistic Weibull DistributionGabriela M. Rodrigues0Edwin M. M. Ortega1Gauss M. Cordeiro2Roberto Vila3Department of Exact Sciences, University of São Paulo, Piracicaba 13418-900, BrazilDepartment of Exact Sciences, University of São Paulo, Piracicaba 13418-900, BrazilDepartment of Statistics, Federal University of Pernambuco, Recife 50670-901, BrazilDepartment of Statistics, University of Brasilia, Brasilia 70910-900, BrazilWe define a new quantile regression model based on a reparameterized exponentiated odd log-logistic Weibull distribution, and obtain some of its structural properties. It includes as sub-models some known regression models that can be utilized in many areas. The maximum likelihood method is adopted to estimate the parameters, and several simulations are performed to study the finite sample properties of the maximum likelihood estimators. The applicability of the proposed regression model is well justified by means of a gastric carcinoma dataset.https://www.mdpi.com/2227-7390/11/6/1518censored datahazard functionodd log-logistic Weibullstatistical reparameterizationsurvival function |
| spellingShingle | Gabriela M. Rodrigues Edwin M. M. Ortega Gauss M. Cordeiro Roberto Vila Quantile Regression with a New Exponentiated Odd Log-Logistic Weibull Distribution Mathematics censored data hazard function odd log-logistic Weibull statistical reparameterization survival function |
| title | Quantile Regression with a New Exponentiated Odd Log-Logistic Weibull Distribution |
| title_full | Quantile Regression with a New Exponentiated Odd Log-Logistic Weibull Distribution |
| title_fullStr | Quantile Regression with a New Exponentiated Odd Log-Logistic Weibull Distribution |
| title_full_unstemmed | Quantile Regression with a New Exponentiated Odd Log-Logistic Weibull Distribution |
| title_short | Quantile Regression with a New Exponentiated Odd Log-Logistic Weibull Distribution |
| title_sort | quantile regression with a new exponentiated odd log logistic weibull distribution |
| topic | censored data hazard function odd log-logistic Weibull statistical reparameterization survival function |
| url | https://www.mdpi.com/2227-7390/11/6/1518 |
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